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Specifications and Common Mistakes

- Specifications and Common Mistakes:

- Misspellings or punctuation mistakes,incorrect formulas, abnormal display.

- Incorrect links, empty cells, or wrong formats.

- Chinese characters in English context.

- Minor inconsistencies between the UI and descriptions.

- Low writing fluency that does not affect understanding.

- Incorrect version numbers, including software package names and version numbers on the UI.

Usability

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- Incorrect or missing key steps.

- Missing main function descriptions, keyword explanation, necessary prerequisites, or precautions.

- Ambiguous descriptions, unclear reference, or contradictory context.

- Unclear logic, such as missing classifications, items, and steps.

Correctness

- Correctness:

- Technical principles, function descriptions, supported platforms, parameter types, or exceptions inconsistent with that of software implementation.

- Incorrect schematic or architecture diagrams.

- Incorrect commands or command parameters.

- Incorrect code.

- Commands inconsistent with the functions.

- Wrong screenshots.

- Sample code running error, or running results inconsistent with the expectation.

Risk Warnings

- Risk Warnings:

- Lack of risk warnings for operations that may damage the system or important data.

Content Compliance

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- Contents that may violate applicable laws and regulations or geo-cultural context-sensitive words and expressions.

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Problem description

Describe the bug so that we can quickly locate the problem.

mindspore.ops.silu

mindspore.ops.silu(input)[source]

Computes Sigmoid Linear Unit of input element-wise. The SiLU function is defined as:

SiLU(x)=xσ(x),

where x is an element of the input, σ(x) is Sigmoid function.

sigma(xi)=11+exp(xi),

SiLU Function Graph:

../../_images/SiLU.png
Parameters

input (Tensor) – input is x in the preceding formula. Input with the data type float16 or float32.

Returns

Tensor, with the same type and shape as the input.

Raises

TypeError – If dtype of input is neither float16 nor float32.

Supported Platforms:

Ascend GPU CPU

Examples

>>> import mindspore
>>> from mindspore import Tensor, ops
>>> import numpy as np
>>> input = Tensor(np.array([-1, 2, -3, 2, -1]), mindspore.float16)
>>> output = ops.silu(input)
>>> print(output)
[-0.269  1.762  -0.1423  1.762  -0.269]